Recommending one or more concepts related to a current analytic activity of a user
Abstract
Methods and apparatus are provided for recommending one or more concepts related to a current analytic activity of a user. One or more concepts related to a current analytic activity of a user are recommended by maintaining a logical record of analytic activity of the user by recording one or more visual analytic actions performed by a user; generating a context model for a plurality of the existing notes containing the concepts, wherein the context model for a given existing note represents information interests of the user; determining a weight for each of the plurality of concepts, wherein a given weight characterizes a relevance of a corresponding concept to the current analytic activity; and recommending one or more concepts based on the determined weight. The weight for a given concept is based on the context model for the given concept and a context model for the current analytic activity. The context model for the given concept represents the information interests of the user at a time surrounding the point when the user recorded the corresponding existing note.
Claims
exact text as granted — not AI-modified1 . A method for recommending one or more concepts related to a current analytic activity of a user, comprising:
recording one or more visual analytic actions performed by said user to maintain a logical record of analytic activity of said user; generating a context model for a plurality of said existing notes containing said concepts, wherein said context model for a given existing note represents information interests of said user; determining a weight for each of said plurality of concepts, wherein a given weight characterizes a relevance of a corresponding concept to said current analytic activity; and recommending, in response to one or more of said visual analytic actions, one or more concepts based on said determined weight.
2 . The method of claim 1 , wherein said weight for a given concept is based on said context model for said given concept and a context model for said current analytic activity.
3 . The method of claim 1 , wherein said context model for said given existing note represents said information interests of said user at a time surrounding the point when the user recorded said corresponding existing note.
4 . The method of claim 1 , wherein said context model is based on a semantic model of information interests of said user.
5 . The method of claim 1 , wherein said context model is represented as a weighted set of action concepts.
6 . The method of claim 5 , wherein a relevance score of a given existing note is based on a specificity of said action concepts.
7 . The method of claim 5 , wherein a relevance score of a given existing note is based on a logical recency of said action concepts.
8 . The method of claim 5 , wherein said weighted set of action concepts is extracted from said analytic activity of said user by spreading activation over a representation of said analytic activity of said user.
9 . The method of claim 5 , wherein said weight W c for a given action concept c is computed as follows:
W
c
=
s
c
×
(
w
b
×
∑
i
=
1
b
d
i
+
w
f
×
∑
i
=
1
f
d
i
)
where s c is a specificity weight of the action concept c; b and f are lengths of back and forward traces, respectively; w b and w f are weights for the forward and back traces; and d i is a normalized distance of an exploration action (i) from an end of a trace for a current view or note.
10 . The method of claim 5 , wherein said weight W(e i ) for a given concept entity e i is computed as
W
(
e
i
)
=
∑
k
=
1
n
d
(
T
k
)
,
where n is a number of relevant notes and d(T) is a relevance score for a given existing note (T).
11 . The method of claim 10 , wherein said weights of a plurality of said concepts are optionally used to determine a font height for displaying each concept.
12 . The method of claim 1 , further comprising the steps of:
updating said context model of said current analytic activity after each user action; determining said weight for each of said plurality of concepts using said newly updated context model to represent said current analytic activity; and recommending said one or more concepts based on said determined weights.
13 . The method of claim 1 , wherein said context model for a given existing note comprises text of said existing note.
14 . A system for recommending one or more concepts related to a current analytic activity of a user, comprising:
a memory; and at least one processor, coupled to the memory, operative to: record one or more visual analytic actions performed by said user to maintain a logical record of analytic activity of said user; generate a context model for a plurality of said existing notes containing said concepts, wherein said context model for a given existing note represents information interests of said user; determine a weight for each of said plurality of concepts, wherein a given weight characterizes a relevance of a corresponding concept to said current analytic activity; and recommend, in response to one or more of said visual analytic actions, one or more concepts based on said determined weight.
15 . An article of manufacture for recommending one or more concepts related to a current analytic activity of a user, comprising a machine readable storage medium containing one or more programs which when executed implement the steps of:
recording one or more visual analytic actions performed by said user to maintain a logical record of analytic activity of said user; generating a context model for a plurality of said existing notes containing said concepts, wherein said context model for a given existing note represents information interests of said user; determining a weight for each of said plurality of concepts, wherein a given weight characterizes a relevance of a corresponding concept to said current analytic activity; and recommending, in response to one or more of said visual analytic actions, one or more concepts based on said determined weight.
16 . The article of manufacture of claim 15 , wherein said weight for a given concept is based on said context model for said given concept and a context model for said current analytic activity.
17 . The article of manufacture of claim 15 , wherein said context model for said given existing note represents said information interests of said user at a time surrounding the point when the user recorded said corresponding existing note.
18 . The article of manufacture of claim 15 , wherein said context model is based on a semantic model of information interests of said user.
19 . The article of manufacture of claim 15 , wherein said context model is represented as a weighted set of action concepts.
20 . The article of manufacture of claim 19 , wherein a relevance score of a given existing note is based on a specificity of said action concepts.
21 . The article of manufacture of claim 19 , wherein a relevance score of a given existing note is based on a logical recency of said action concepts.
22 . The article of manufacture of claim 19 , wherein said weighted set of action concepts is extracted from said analytic activity of said user by spreading activation over a representation of said analytic activity of said user.
23 . The article of manufacture of claim 19 , wherein said weight W c for a given action concept c is computed as follows:
W
c
=
s
c
×
(
w
b
×
∑
i
=
1
b
d
i
+
w
f
×
∑
i
=
1
f
d
i
)
where s c is a specificity weight of the action concept c; b and f are lengths of back and forward traces, respectively; w b and w f are weights for the forward and back traces; and d i is a normalized distance of an exploration action (i) from an end of a trace for a current view or note.
24 . The article of manufacture of claim 19 , wherein said weight W(e i ) for a given concept entity e i is computed as
W
(
e
i
)
=
∑
k
=
1
n
d
(
T
k
)
,
where n is a number of relevant notes and d(T) is a relevance score for a given existing note (T).
25 . The article of manufacture of claim 24 , wherein said weights of a plurality of said concepts are optionally used to determine a font height for displaying each concept.Join the waitlist — get patent alerts
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